| This model is fine tuned on the IEMOCAP dataset. We applied volume normalization and data augmentation (noise injection, pitch shift and audio stretching). Also, this is a speaker independent model: We use Ses05F in the IEMOCAP dataset as validation speaker and Ses05M as test speaker. | |
| The initial pre-trained model is facebook/wav2vec2-base. The fine tune dataset only contains 4 common emotions of IEMOCAP (happy, angry, sad, neutral), *without frustration*. The audios are either padded or trimed to 8-sec-long before fine tuning. | |
| After **10** epoches of training, the validation accuracy is around **67%**. | |
| In order to impliment this model: Please run the following code in a python script: | |
| ``` | |
| from transformers import AutoFeatureExtractor, AutoModelForAudioClassification | |
| import librosa | |
| import torch | |
| target_sampling_rate = 16000 | |
| model_name = 'canlinzhang/wav2vec2_speech_emotion_recognition_trained_on_IEMOCAP' | |
| audio_path = your_audio_path | |
| #build id and label dicts | |
| id2label = {0:'neu', 1:'ang', 2:'sad', 3:'hap'} | |
| label2id = {'neu':0, 'ang':1, 'sad':2, 'hap':3} | |
| feature_extractor = AutoFeatureExtractor.from_pretrained(model_name) | |
| model = AutoModelForAudioClassification.from_pretrained(model_name) | |
| y_ini, sr_ini = librosa.load(audio_path, sr=target_sampling_rate) | |
| inputs = feature_extractor(y_ini, sampling_rate=target_sampling_rate, return_tensors="pt") | |
| logits = model(**inputs).logits | |
| predicted_class_ids = torch.argmax(logits).item() | |
| pred_class = id2label[predicted_class_ids] | |
| print(pred_class) | |
| ``` |